Evidence map›Paper›PMID 37495975›Full record

ArticleBMC cancer2023

Predicting survival for patients with mesothelioma: development of the PLACE prognostic model.

Yuan Zhang, Nan Li, Ran Li, Yumei Gu, Xiaofang Liu, Shu Zhang

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Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuan Zhang *Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongtinan Rd, Chaoyang District, Beijing, 100020, China.
Nan Li *Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongtinan Rd, Chaoyang District, Beijing, 100020, China.
Ran Li *Department of Respiratory and Critical Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China.
Yumei GuDepartment of Pathology, Beijing Chao-Yang Hospital , Capital Medical University, 8 Gongtinan Rd, Chaoyang District, Beijing, 100020, China.
Xiaofang Liu *Department of Respiratory and Critical Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China.
Shu Zhang *Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongtinan Rd, Chaoyang District, Beijing, 100020, China. zhangshu530@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe overall survival of patients with mesothelioma is poor and heterogeneous. At present, the prediction model for Chinese patients needs to be improved. We sought to investigate predictors of survival in malignant pleural mesothelioma and develop prognostic prediction models.

methodsThis Two-center retrospective cohort study recruited patients with pathologically diagnosed mesothelioma at Beijing Chao-Yang Hospital and Beijing Tong-Ren Hospital. We developed a new prognostic prediction model based on COX multivariable analysis using data from patients who were recruited from June 1, 2010 to July 1, 2021 in Beijing Chao-Yang Hospital (n = 95, development cohort) and validated this model using data from patients recruited from July 18, 2014 to May 9, 2022 in Beijing Tong-Ren Hospital (n = 23, validation cohort). Receiver operating characteristic analysis was used to estimate model accuracy.

resultsThe parameters in this new model included PLT > 289.5(10^9/L) (1 point), Lymphocyte > 1.785(10^9/L) (-1point), Age > 73 years old (1 point), Calcium > 2.145(mmol/L) (-1point), Eastern Cooperative Oncology Group performance status (ECOG PS) > 2 (2 points). When the sum of scores < 0, it is recognized as a low-risk group; when the score is 0 ~ 3, it is recognized as a high-risk group. The survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (hazard ratio [HR], 3.878; 95% confidence interval [CI], 2.226-6.755; P < 0.001). The validation group had similar results (HR,3.574; 95%CI,1.064-12.001; P = 0.039). Furthermore, the areas under the curve 6 months after diagnosis in the two cohorts were 0.900 (95% CI: 0.839-0.962) and 0.761 (95% CI: 0.568-0.954) for development and validation cohorts, respectively.

conclusionWe developed a simple, clinically relevant prognostic prediction model for PLACE by evaluating five variables routinely tested at the time of diagnosis. The predictive model can differentiate patients of Chinese ethnicity into different risk groups and further guide prognosis.

Indexed as

MesotheliomaMesothelioma, MalignantPleural NeoplasmsAgedHumansPrognosisRetrospective StudiesCalciumLymphocyteMalignant pleural mesotheliomaPrediction modelPrognosis

Identifiers

PMID37495975
PMCPMC10369846

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.